CN102721987A - Method for prewarning Doppler radar remote sensing strong storm - Google Patents

Method for prewarning Doppler radar remote sensing strong storm Download PDF

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CN102721987A
CN102721987A CN2012101909262A CN201210190926A CN102721987A CN 102721987 A CN102721987 A CN 102721987A CN 2012101909262 A CN2012101909262 A CN 2012101909262A CN 201210190926 A CN201210190926 A CN 201210190926A CN 102721987 A CN102721987 A CN 102721987A
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storm
constantly
identification
early warning
storms
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韩雷
卞继方
宋珍妮
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Ocean University of China
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Abstract

The invention relates to an automatic prewarning method for strong storms. Doppler weather radar data is used. According to identification on storms, false combination of storms can be successfully identified by adopting a method based on mathematical morphology, namely expansion operation and corrosion operation are comprehensively used during multi-threshold identification, and relatively near storms can be separated from a storm cluster; then, according to tracking of the storms, the tracking process is greatly simplified by adopting a sequential monte carlo method, and situations of splitting, combination and leakage detection of the storms can be handled; and finally, the storms are prewarned, namely linear fitting extrapolation prewarning is realized by being combined with a motion vector field obtained by an optical flow method. According to the method, complicated situations that radar echo is densely distributed and frequently split and combined can be handled. The method has the advantages that a prewarning software system is arranged in provincial and municipal weather stations; high-convection weather prewarning service is provided; and the urgent demands for preventing and reducing disasters in China are met.

Description

The method for early warning of radar Doppler remote sensing strong storm
Technical field
The invention belongs to the remote sensing monitoring technology of atmospheric environment, be specifically related to a kind of automatic early warning method of radar Doppler remote sensing strong storm.
Background technology
Strong convective weather is also claimed strong storm, is one of main diastrous weather, comprises thunder and lightning, hail, thunderstorm, strong wind etc., and it can produce huge social harm when taking place.Therefore, monitoring and the early warning to strong storm has significant social and economic implications.Because the strong storm space scale is less, the duration is shorter, utilization routine observation means are difficult to it is effectively monitored.And Doppler radar is the main remote sensing monitoring means of strong storm at present, has very high spatial and temporal resolution, and can observe the three-D space structure of storm.
Strong storm form with three-dimensional echo on weather radar occurs.When echo distributes sparsely, and change when slow, be easier to carry out early warning.But to the densely distributed storm of echo bunch, or echo changes violently, and the frequent complex situations such as division and merging that occur can be brought very big difficulty to early warning, and existing method can't be handled.But along with climate warming, extreme strong convection hazard weather takes place frequently, and the appearance of these echo complex distribution situation is also more frequent.Therefore,, division densely distributed to these echoes merges complex situations such as frequent, and the storm automatic early warning method that proposition can be handled these problems is crucial.
Summary of the invention
The object of the invention provides a kind of automatic early warning method of strong storm, to remedy the deficiency of prior art.
The present invention adopts the method based on mathematical morphology at first in the identification of storm, can solve false consolidation problem preferably, and can from the storm of complicacy bunch, isolate the storm monomer.Then, in the tracking of storm, introduce sequential monte carlo method, make tracing process greatly simplify.Not only can in the process of following the trail of, handle the division and the merging of storm simultaneously, can also handle the test leakage situation of storm.At last, to the early warning of storm,, carry out linear fit extrapolation early warning in conjunction with the motion vector field that optical flow method obtains.
Strong storm method for early warning step of the present invention is following:
1. data pre-service: the radar base data with after the quality control is interpolated under the three-dimensional geographic coordinate.
2. the identification of strong storm: the present invention proposes storm recognition methods, at first use first order threshold value to carry out single threshold identification based on mathematical morphology; Secondly, the storm that identification obtains is carried out the corrosion operation, to eliminate false the merging; Then, use high one-level threshold value to discern, and the storm that identification is obtained. body carries out expansive working, contact each other in the process that is expanding when the border of storm, or when having touched the border of storm of original low Threshold Identification, then stop expansion process; At last, use the threshold value of higher level to discern one by one, and in the identifying of each grade threshold value, carry out corrosion and expansive working.
3. the tracking of strong storm: the present invention is applied to sequential monte carlo method the tracking of storm; Its basic process is a process of carrying out iteration along with time series; Each iteration comprised for three steps: sampling, and prediction and measurement, thus obtain needed multidate information in real time.
The present invention adopts the combined reflected rate factor graph of radar picture to the tracking of adjacent moment storm.Each particle of sampling gained all will pass through the first-order linear system model, carries out one-step prediction.
(1) tracking of adjacent moment storm.Definition
Figure BSA00000732423800021
is that incident: t m storm constantly come by t-1 k storm development constantly.As observation Y tAfter the arrival, the calculating incident
Figure BSA00000732423800022
The probability that takes place:
p ( θ t m , k | Y t ) = max ( NF m , k A m , NF m , k A k ) , m = 1 . . N t , k = 1 . . N t - 1
Wherein, A mBe the t area of m storm constantly, A kBe the t-1 area of k storm constantly, NF M, kBe meant from all particles that t-1 moment k storm samples out, after one-step prediction, fall into the t number of particles of m storm constantly, if
Figure BSA00000732423800024
Greater than threshold value T r, the present invention is made as 0.5, and then t m storm constantly is considered to come by k the storm development in the t-1 moment.
(2) processing of storm test leakage.Use delay logic to solve this problem, promptly do not make a policy, but postpone one or more moment, when fully many information is arranged, just make a policy at current time.At first, at t-2 constantly, storm is done uniform sampling and carried out one-step prediction; Then, at t-1 constantly, the new particle that one-step prediction is obtained upgrades weight w, and method is following:
Figure BSA00000732423800025
J=1..NP, wherein, I J, t-1Be the value of the reflectivity factor that actual observation arrives in particle j present position, NP is the number of particles of constantly from storm, sampling at t-2, T ZminBe the 1st grade of reflectivity factor threshold value in the storm algorithm.The weight of these particles of normalization makes
The storm virtual center of mass position of estimating according to
Figure BSA00000732423800027
.Then;
Figure BSA00000732423800028
carries out double sampling and carries out one-step prediction to the particle collection; Can judge t-2 according to
Figure BSA00000732423800029
; T-1; Whether the relation with the t moment storm test leakage promptly occurred.
(3) division, the processing that merges.Value according to
Figure BSA000007324238000210
; And from the relative position on particle and storm border; If easy judgement t two storms constantly all are by the same storm development in the t-1 moment and next, think that then division has taken place this storm in the t-1 moment; Similarly, can differentiate the situation of merging
4. the early warning of strong storm: at first,, calculate its combined reflected rate factor graph picture, use optical flow method to calculate motion vector field to the radar data of the nearest moment and previous moment; To the storm of each identification, calculate the average motion vector (V of its overlay area then Ave); At last, use V AveWith the centroid position of storm, the following storm position constantly of early warning; To property parameters such as the rising of storm, volumes, use the early warning of first-order linear model.
5. result's output: be not difficult above method is compiled into software systems on computers, show with image format.
Advantage of the present invention: the falseness that can effectively eliminate storm merges, and can under echo distributes the situation of comparatively dense, correctly discern storm; In the storm tracing process, take into full account possible storm test leakage situation, efficiently solved the division and the consolidation problem of storm; Be used in combination the echo motion vector field and the first-order linear model carries out early warning, obtain more stable result.
Description of drawings
Fig. 1 is the schematic flow sheet of the method for early warning of strong storm of the present invention.
Embodiment
Like Fig. 1 is that the performing step of strong storm method for early warning of the present invention is following:
1. data pre-service.At first carry out quality control, remove the radar base data that quality has serious problems.Adopt then radially with the orientation on nearest-neighbors method and vertical linearity interpolation method, the base data under the polar coordinate system is interpolated under the three-dimensional geographic coordinate.
2. the identification of strong storm.Key step is following:
1) uses first order threshold value T Z, min, use three-dimensional clustering procedure to carry out the identification of storm.
2) to the 1st) the three-dimensional storm that obtains of step identification once corrodes operation, and eliminate the faint connection between the adjacent storm, thereby solve the false problem that merges.
3) use threshold value T successively Z, 1=T Z, min+ (i-1) * and 5dBZ, i=2 ... N Thresh, carry out the identification of storm, and the result of identification corroded and expansive working, thereby isolate strong and weak each uneven storm monomer in the storm bunch step by step.
3. the tracking of strong storm: the principle of sequential monte carlo method is applied to the tracking of storm, its basic process be one along with time series is carried out the process of iteration, each iteration comprised for three steps: sampling, prediction and measuring.
(1) to the tracking of adjacent moment storm
A) sampling.To t-1 each identified storm constantly, in its region covered, carry out importance sampling, consider the size of the reflectivity factor at each place, sample present position during sampling, the population of sampling is:
NP k＝floor(A k)，k＝1...N i-1
Wherein, A kBe the projected area of k storm, size is at 30～1000km 2N T-1Be the constantly detected storm sum of t-1.Floor (A k) be meant to get and be no more than A kMaximum integer.
To of position coordinates x=(x, y) expression of each particle state with its place.Simultaneously, the identification number and an identical weight w that distribute storm under it for each particle.The identification number of sudden and violent body is used for distinguishing the particle that belongs to different storms in follow-up prediction with measuring phases.To t-1 k storm constantly, the particle assembly that obtains of therefrom sampling can be expressed as: { x j , t - 1 , w j , t - 1 = 1 / NP , k } j = 1 NP k .
B) prediction.Through making a particle all pass through the first-order linear system model:
x t＝x t-1+V t×Δt+ω(t)
Obtain in next one predicted position constantly.Wherein, V tBe point (x, the motion vector of y) locating that calculates by optical flow method.Δ t is the SI of radar, generally is 5～10 minutes.ω (t) is that average is 0, and variance is σ 2Gaussian noise.
C) measure.Definition
Figure BSA00000732423800042
is that incident: t m storm constantly come by t-1 k storm development constantly.As observation Y tAfter the arrival, the calculating incident
Figure BSA00000732423800043
The probability that takes place:
p ( θ t m , k | Y t ) = max ( NF m , k A m , NF m , k A k ) , m = 1 . . N t , k = 1 . . N t - 1
Wherein, A mBe the t area of m storm constantly, A kIt is the t-1 area of k storm constantly.NF M, kBe meant from all particles that t-1 moment k storm samples out, after one-step prediction, fall into the t number of particles of m storm constantly.
If
Figure BSA00000732423800045
Greater than threshold value T r(T r=0.5), then t m storm constantly be considered to by t-1 k storm constantly develop and.
(2) processing of storm test leakage
At first, at t-2 constantly, storm is done uniform sampling and carried out one-step prediction; Then, at t-1 constantly, the new particle that one-step prediction is obtained upgrades weight w, and method is following:
w j , t - 1 = I j , t - 1 T zmm , j = 1 . . NP
Wherein, I J, t-1Be the value of the reflectivity factor that actual observation arrives in particle j present position, NP is the number of particles of constantly from storm, sampling at t-2, T ZminBe the 1st grade of reflectivity factor threshold value in the storm algorithm.The weight of these particles of normalization makes Σ j = 1 NP w j , t - 1 = 1 .
At last, the storm virtual center of mass position of estimating according to
Figure BSA00000732423800048
.
Figure BSA00000732423800049
carries out double sampling and carries out one-step prediction to the particle collection; Can judge t-2 according to
Figure BSA000007324238000410
; T-1; Whether the relation with the t moment storm test leakage promptly occurred.
(3) division and the processing that merges
Value according to
Figure BSA000007324238000411
; And from the relative position on particle and storm border; If easy judgement t two storms constantly all are by the same storm development in the t-1 moment and next, think that then division has taken place this storm in the t-1 moment; Similarly, can differentiate the situation of merging.
4. the early warning of strong storm.Implementation method is following:
(1) early warning of storm movement velocity.At first, use optical flow method to calculate motion vector field; To the storm of each identification, calculate the average motion vector (V of its overlay area then Ave); At last, use V AveWith the centroid position of storm, the following storm position constantly of early warning.
(2), use the early warning of first-order linear model to property parameters such as the rising of storm, volumes.
5. result's output: be not difficult above method is compiled into software systems on computers, show with image format.
Use method of the present invention, can obtain the 5-10 minute real-time surveillance map picture of radar at interval, and provide identification, tracking and the early warning object information of storm simultaneously, the important reference frame of early warning raising for strong convective weather has broad application prospects.

Claims (4)

1. the automatic early warning method of a strong storm, its step is following:
(1), the data pre-service: at first carry out quality control, remove the radar base data that quality has serious problems, adopt then radially with the orientation on nearest-neighbors method and vertical linearity interpolation method, the base data under the polar coordinate system is interpolated under the three-dimensional geographic coordinate;
(2), the identification of strong storm: the present invention proposes storm recognition methods, at first use first order threshold value to carry out single threshold identification based on mathematical morphology; Secondly, the storm that identification obtains is carried out the corrosion operation, to eliminate false the merging; Then, use high one-level threshold value to discern, and the storm that identification is obtained. body carries out expansive working, contact each other in the process that is expanding when the border of storm, or when having touched the border of storm of original low Threshold Identification, then stop expansion process; At last, use the threshold value of higher level to discern one by one, and in the identifying of each grade threshold value, carry out corrosion and expansive working;
(3), the tracking of strong storm: the present invention is applied to sequential monte carlo method the tracking of storm; Its basic process is a process of carrying out iteration along with time series; Each iteration comprised for three steps: sampling, and prediction and measurement, thus obtain needed multidate information in real time; The present invention adopts the combined reflected rate factor graph of radar picture to the tracking of adjacent moment storm, and each particle of sampling gained all will pass through the first-order linear system model, carries out one-step prediction;
● the tracking of adjacent moment storm: definition
Figure FSA00000732423700011
Be that incident: t m storm constantly come by t-1 k storm development constantly, as observation Y tAfter the arrival, the calculating incident
Figure FSA00000732423700012
The probability that takes place:
p ( θ t m , k | Y t ) = max ( NF m , k A m , NF m , k A k ) , m = 1 . . N t , k = 1 . . N t - 1
Wherein, A mBe the t area of m storm constantly, A kBe the t-1 area of k storm constantly, NF M, kBe meant from all particles that t-1 moment k storm samples out, after one-step prediction, fall into the t number of particles of m storm constantly, if Greater than threshold value T r, the present invention is made as 0.5, and then t m storm constantly is considered to come by k the storm development in the t-1 moment;
● the processing of storm test leakage: use delay logic to solve this problem, promptly do not make a policy, but postpone one or more moment, when fully many information is arranged, just make a policy at current time;
● division, the processing that merges: according to the relative position on particle and storm border; Use a kind of method of geometry to judge; If t two storms constantly all are by the same storm development in the t-1 moment and next, think that then division has taken place t-1 this storm constantly; Similarly, can differentiate the situation of merging;
(4), the early warning of strong storm: at first,, calculate its combined reflected rate factor graph picture, use optical flow method to calculate motion vector field to the radar data of the nearest moment and previous moment; To the storm of each identification, calculate the average motion vector (V of its overlay area then Ave); At last, use V AveWith the centroid position of storm, the following storm position constantly of early warning; To property parameters such as the rising of storm, volumes, use the early warning of first-order linear model;
(5), result's output: be not difficult above method is compiled into software systems on computers, show with image format.
2. strong storm method for early warning as claimed in claim 1; The method based on mathematical morphology has been adopted in the three-dimensional identification that it is characterized in that storm in the step (2); Promptly through in many Threshold Identification process; The comprehensive use expanded and the corrosion operation, and the falseness that can successfully identify storm merges, and from storm bunch, isolates at a distance of nearer storm.
3. strong storm method for early warning as claimed in claim 1; It is characterized in that having used sequential monte carlo method in the step (3); Use
Figure FSA00000732423700021
as key index; Judge the different corresponding relations of storm constantly; Simplify tracing process, can handle the division and the merging of storm simultaneously, can also handle the test leakage situation of storm.
4. strong storm method for early warning as claimed in claim 1 is characterized in that the motion vector field that the combination optical flow method obtains in the step (4), carries out linear fit extrapolation early warning.
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Application publication date: 20121010